A Methodological Evaluation of Emergency Care Systems in Kenya: A Time-Series Forecasting Model for Clinical Outcome Metrics.

{ "background": "Emergency care systems in sub-Saharan Africa face significant operational challenges, yet robust methodological frameworks for evaluating their clinical performance over time are scarce. Existing assessments often rely on cross-sectional data, lacking the capacity for predictive analytics to inform resource allocation and system strengthening. ", "purpose and objectives": "This case study aimed to develop and validate a time-series forecasting model for key clinical outcome metrics within a Kenyan emergency unit context, providing a methodological proof-of-concept for routine performance monitoring. ", "methodology": "A case study design was employed, utilising retrospective, de-identified patient outcome data from a major referral hospital. The core methodology involved constructing a Seasonal AutoRegressive Integrated Moving Average (SARIMA) model, specified as \ (B) \ (Bˢ) \ᵈ\Ds Yt = \ (B) \ (Bˢ) \ₜ, to forecast monthly mortality rates. Model diagnostics included analysis of autocorrelation and partial autocorrelation functions, with forecasting accuracy evaluated using mean absolute percentage error (MAPE). ", "findings": "The SARIMA (1, 1, 1) (0, 1, 1) 12 model provided the best fit for forecasting mortality, with a MAPE of 8. 7% (95% CI: 6. 2, 11. 5) on the test set. Forecasts indicated a persistent upward trend in mortality rates over a six-month projection period, highlighting a critical pressure point requiring intervention. ", "conclusion": "The study demonstrates the feasibility and utility of time-series forecasting as a methodological tool for proactive emergency care systems evaluation in resource-constrained settings. The model offers a data-driven approach to anticipate clinical workload and outcome trajectories. ", "recommendations": "Emergency care networks should integrate routine time-series analytics into their performance dashboards. Further research should focus on incorporating exogenous variables (e. g. , staffing levels, seasonal disease prevalence) to improve model specificity and generalisability across different facility types. ", "key words": "Emergency medical services, forecasting, time